Embodiments of the disclosure relate to eye tracking. More specifically, aspects of the disclosure relate to tracking eyes in video using a novel approach that is both energy and time-efficient. In recent years, the increased sophistication and accessibility of eye tracking technologies have generated a great deal of interest in the commercial sector. Applications include web usability, advertising, sponsorship, package design, gaming, and automotive engineering. However, current eye tracking algorithms have a number of limitations. For example, current eye trackers tend to require the entire face to be within the field of view (FOV). Problems may arise if part of the face is covered, e.g. by hair, or the FOV is too close such that only part of the face is shown. Because conventional eye trackers require detecting the whole face before locating the eyes in the face, the rate of the eye tracking is slowed or hampered due to the additional processing of tracking the face first. Furthermore, conventional eye trackers require a timely re-initialization phase whenever eye tracking is lost, e.g., a person leaves the FOV and then comes back, or the person turns his face away and back again.
Accordingly, a need exists for improved eye tracking.